Statistical sampling security methodology for self-scanning checkout system

ABSTRACT

A statistical basis for use in a self-scanning checkout system determines how many items to check in a shopper&#39;s shopping cart for incorrect or missing scans as well as which particular or types of items to check to determine if they were properly scanned, if the shopper is determined to be audited. The present invention does not audit every customer, but rather determines whether a given shopper or customer is to be audited on a given shopping trip based upon obtaining a minimum checkout loss for such customer. The methodology determines how many items to check for a given shopper as well as which particular items to check for that shopper. The following factors attempt to model the real world of shopping and may be considered, alone or in varying combinations, in determining the number of items to check for a particular shopping transaction: shopper frequency; queue length; prior audit history; store location; time of day, day of week, date of year; number of times items are returned to shelf during shopping; dwell time between scans; customer loyalty; store shopping activity and other factors. Using statistical decision theory for auditing policies a minimum loss per shopper transaction improves the security and reduces the labor of self-check out without being too intrusive to customers.

This application is a continuation-in-part of Ser. No. 08/787,728, filedJan. 24, 1997, now U.S. Pat. No. 5,877,485, which parent applicationclaims the benefit of U.S. Provisional Application Ser. No. 60/011,054,filed Jan. 25, 1996, assigned to the same assignee as that of thepresent application, and is incorporated fully herein by reference.

BACKGROUND OF THE INVENTION

This invention relates to self-service shopping, and in particular to amethodology for improving the security of a self-service shopping systemby the use of statistical sampling of shoppers and their purchases.

Self-service shopping systems are desired for their ability to offloadservice-oriented functions from human labor forces and provide automaticassistance to the shopper for increase in response time, efficiency,throughput, lower cost, and the like. For example, systems in the priorart provide each shopper with a portable bar code scanning device, whichis used to scan the bar code located on a product in order to determinethe price by accessing a locally stored look-up table and keep a tallylist of all items selected for purchase. When the shopper is finishedselecting items for purchase and scanning the bar codes on the items, heplaces the self-scanner into a recess in a stationary (i.e.,wall-mounted) cradle, wherein a list of items selected is printed outfor a receipt and provided to the shopper. The shopper then brings thelist along with the cart of selected items to a clerk for tender offinal payment and, possibly, an audit of items selected for purchase inorder to ensure that all items selected and placed in the cart wereproperly scanned. This self-service scenario speeds shoppers through thestore quicker than the conventional conveyor belt/cashier environmenttypical in stores today.

Security in a self-service shopping system as described above is a majorconcern of retailers. Shoppers who fail to scan the bar code of an itemplaced in their shopping cart will bring the item home without properpayment, whether such failure to scan the item is intentional orinadvertent. In addition, shoppers may scan an item but place adifferent (i.e. more expensive) item in their cart. Therefore, somemethodology of checking shoppers' purchases must be implemented in orderto satisfy security criteria.

Two goals of a self-checkout system are to increase shopper throughputand to save in labor costs. Ideally, a shopper can scan his items whenselected from the shelf and save scanning time at the checkout line. Inaddition, stores would require less human labor since there is areduction in the number of cashiers required. However, there is still arequirement to scan some items from a shopper's cart if a shopper isdetermined to be audited in order to flag an attempted theft as well asto provide deterrence against pilferage. Thus, some labor is required toscan at least some of the items leaving the store. At one extreme, asystem where every shopper has all of his purchase items re-scanned isnot feasible since there is no net time savings in such a system (allpurchases are scanned by a cashier anyway). There is therefore a need todetermine whether a shopper needs to be audited and how many and whichitems are to be scanned in order to maximize the potential for catchingpilferage, provide maximum deterrence against theft, minimize laborcosts in checking the shoppers' scanned items, maintain the increasedthroughput achieved by the self-checkout system, and avoid the negativeinferences inherently made by shoppers whose items are checked by anexit cashier or security guard.

Prior art proposals for checkout security require a cashier to checkonly certain shoppers, but to scan their entire cart full of goods. Thistype of system is unsatisfactory for those shoppers who are selected forfull checking, since they must wait for the entire cart to be re-scanned(thus defeating the purpose of the self-checkout system), sufferpotential embarrassment at being singled out by the store for securitychecking, etc. Thus, an entirely new methodology is needed to supplantthis security checking system.

SUMMARY OF THE INVENTION

The present invention proposes the implementation of a statistical basisfor use in a self-scanning checkout system for determining whether ashopper or customer should be audited and how many items to check in ashopper's shopping cart for incorrect or missing scans as well as whichparticular or types of items to check to determine if they were properlyscanned. In the present invention, a fraction of the shoppers, dependingupon an algorithm, will be checked by a cashier or security guard, butonly a limited and select number of items will be checked for eachshopper. The present methodology determines how many items to check fora given shopper as well as which particular items to check for thatshopper. The following factors may be considered, alone or in varyingcombinations, in determining the number or type of items to check for aparticular shopping transaction: shopper frequency (the number of timesthe shopper has visited that store), queue length (the length of thecheckout line at that time); prior history (check more items if theshopper has had errors in the past, check less items if the shopper hashad no errors in the past), store location (check more items in storeslocated in areas with a high risk of pilferage); time of day, day ofweek, date of year (determine if pilferage more likely at certain timesof day or year); number of times items are returned to shelf duringshopping; dwell time between scans, and other factors.

In a method aspect of the present invention, provided is a method foruse in a self-service shopping checkout system wherein a shopper isprovided with a self-scanning terminal for the scanning of the bar codeof an item selected for purchase prior to depositing the item into ashopping cart, and wherein a list of items self-scanned by the shopperis compiled and made available to a cashier for surveillance and paymentpurposes. The method performs a security check to determine if theshopper did not likely fail to scan an item prior to depositing the iteminto the shopping cart. The method comprises the steps of determining,as a function of a plurality of input criteria, whether a shopper shouldbe audited and the number of items n to be scanned, selecting from theshopper's cart of items presented for purchase n items to be scanned,scanning a bar code located on each of said n items selected forscanning, allowing the shopping transaction if each item selected forscanning is present on the list of self-scanned items compiled by theshopper, and disallowing the shopping transaction if any item selectedfor scanning is not present on the list of self-scanned items compiledby the shopper. The number of items n is determined as a function of thecriteria mentioned above. The method further comprises the steps ofselecting items at random system checks and determining whether moreitems are needed to be checked until statistical significance isachieved.

In a systems aspect, the present invention comprises several alternativeembodiments. In each embodiment, a self-service shopping checkout systemcomprises a plurality of portable self-checkout devices, wherein each ofthe self-checkout devices is to be used by a customer to scan a bar codelocated on an item to be purchased so as to record therein a list ofsuch items to be purchased. In one embodiment, a stationary dispenserunit is used for the releasable containment of said plurality ofportable self-checkout devices and transmission of data stored in thedevices by wireline to a host computer for processing; and a pluralityof point-of-sale terminals using the host processing to check out thecustomer. In another embodiment, the portable device contains a wirelesstransceiver for transmitting the data stored in the device directly tothe host computer in lieu of storing the data in the device and usingthe dispenser to transmit the stored data to the computer. In stillanother embodiment, the portable device is a dumb terminal forcollecting and storing the shopping data which may be used inconjunction with a kiosk to determine the prices and cost of itemsselected for purchase. The kiosk contains a display and a rack forreceiving the dumb terminal to communicate with the host computer bywireline or a wireless link. In response to customer inputs, the desiredinformation is presented on the display. Alternatively, the customer mayplace the dumb terminal in a cradle at the checkout stand. The cradleloads the data in the dumb terminal into the host computer forprocessing and check out of the customer.

In the present system, each of the portable self-checkout devicescomprises bar code scanning means for scanning and decoding a bar codelocated on an item to be purchased, means for compiling a list of itemsscanned by said customer, and a data output port for allowing transferof said scanned item list to an associated data port located in adispenser external to said portable device or by wireless link directlyto a host computer for processing the shopper's items selected forpurchase. In one embodiment, the dispenser unit of the system comprisesa plurality of device containment slots, each of said slots beingconfigured for releasable containment of a mating self-scanning device,each of said slots having associated therewith a data input portsuitable for mating with a data output port located on a portableself-checkout device, and a printer for printing a tally list of itemsscanned for purchase by said shopper, said tally list being supplied bya self-checkout device after said self-checkout device is returned to adevice containment slot after being used by a shopper, said tally listfurther comprising a bar code encoded with said items scanned by saidshopper, a unique identification record associated with said shopper,and the number of items scanned by said shopper. Each of thepoint-of-sale terminals in the present system comprises bar code readingmeans for reading said two-dimensional bar code from a tally listpresented to a cashier operating said point-of-sale terminal, said barcode reading means providing as output data signals representing saiditems scanned by said shopper, a unique identification record associatedwith said shopper, and the number of items scanned by said shopper. Eachof the point-of-sale terminals in the present system comprises bar codereading means for reading said two-dimensional bar code from a tallylist presented to a cashier operating said point-of-sale terminal, saidbar code reading means providing as output data signals representingsaid items scanned by said shopper, said unique identification recordassociated with said shopper, and said number of items scanned by saidshopper; said bar code reading means also configured so as to scanselect items presented for checking by said cashier; means fordetermining, as a function of said number of items scanned by saidcustomer and an internally stored check number unique to said customer,the number of items n to be scanned by the cashier; means for comparingthe identity of the items scanned by said cashier with the list of itemsscanned by said customer; means for allowing the shopping transaction ifeach item selected for scanning by the cashier is present on the list ofself-scanned items compiled by the shopper; and means for disallowingthe shopping transaction if any item selected for scanning by thecashier is not present on the list of self-scanned items compiled by theshopper.

The present invention may be further enhanced using other auditingfactors as a basis for statistical techniques for self-check out. Amongthe audit factors for such self-checkout are shopper audit history,loyalty of shoppers, regional differences and other factors whichattempt to model the real world to obtain the minimum checkout loss fora shopper. After a determination whether a shopper should be auditedand, if so, how many items should be selected for audit, a shoppingtransaction is allowed according to a statistical decision if theminimum check-out loss for the transaction is less than a thresholddefined by a loss function L(c) and (p), where L(c) is the expectedinventory loss associated with the shopper and (p) is the probability ofperforming an audit on the shopper.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a diagram of the input criteria used in the present invention;

FIG. 2 is a flow chart of the method of the present invention;

FIG. 3 is a block diagram of the system of the present invention;

FIG. 4 is a block diagram of the self-scanning terminal of the presentinvention; and

FIG. 5 is a block diagram of the host computer of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT

In FIG. 1, the present methodology determines how many items to checkfor a given shopper transaction as well as which particular items tocheck for that shopper, using the following input criteria which may beconsidered, alone or in varying combinations: shopper's prior history A(check more items if the shopper has had errors in the past, check lessitems if the shopper has had no errors in the past); store location anddemographics B (check more items in stores located in areas with a highrisk of pilferage); number of shopper's in checkout queues or queuelength C (the length of the checkout line at that time); shopper's scanhabits D or the number of times items are returned to shelf duringshopping, and dwell time between scans; time of day, day of week date ofyear E (determine if pilferage more likely at certain times of day oryear); and types of items scanned F. Other factors may include, shoppingfrequency (the number of times the shopper has visited that store).

In FIG. 2, a method for performing a security check to determine if theshopper did not likely fail to scan an item prior to depositing the iteminto the shopping cart comprises the steps of G generating a pluralityof input criteria for self-check out; H determining if the customer isto be audited, i.e items to be re-scanned; I performing a statisticaldetermination of which items (R) to be selected and scanned; Jperforming a statistical determination of how many items of the selecteditems to check in a shopper's basket; K generating a list of items tocheck; L selecting from the shopper's cart of items presented forpurchase n items to be scanned including M the step of properly scanninga bar code located on each of said n items selected for scanning fromthe chart for proper scanning; N allowing the shopping transaction ifeach item selected for scanning is present on the list of self-scanneditems compiled by the shopper; disallowing the shopping transaction ifany item selected for scanning is not present on the list ofself-scanned items compiled by the shopper; and P adjusting the securityparameters for the next shopping transaction and providing the adjustedsecurity parameters to the input criteria. In an alternate embodiment,the system may advise the cashier or security guard as to the number ofitems to check in step J without specifying which items to check.

FIG. 3 illustrates a block diagram of the secure self-service shoppingsystem of the present invention. The system 2 comprises, at the toplevel, a scanner dispenser 2, a host computer system 4, and a pluralityof point of sale (POS) terminals 6. The host computer 4 is a standardcomputer system well known in the prior art and found in retailestablishments such as supermarkets for controlling operations of thesupermarket, as modified as described below to carry out the methods andfunctions of the present invention. In particular, the host computer 4is capable of interfacing with the scanner dispenser 2 for datacommunications therebetween in accordance with the present invention aswill be described more fully below. Likewise, each POS terminal 6 is astandard POS computer system well known in the prior art and found inretail establishments such as supermarkets for controlling checkoutfunctions of the supermarket, such as purchase item entry and paymenttender functions, as modified as described below to carry out themethods and functions of the present invention. In particular, the POSterminal 6 is capable of interfacing with the host computer 4 for datacommunications therebetween in accordance with the present invention aswill be described more fully below.

The scanner dispenser 2 is a stationary, i.e., wall-mounted chassis,which comprises a plurality of interface slots 10 configured tophysically and electrically mate with an associated portable scanningterminal 100, shown in detail in FIG. 4. Each terminal 100 is placedwithin an associated recess in the dispenser 2 for data transferfunctions, battery recharge, etc., after the shopper has used thescanning terminal for self-service scanning functions. After data hasbeen transferred between the terminal 100 and the dispenser 2, as willbe described below, and the terminal power supply (i.e. battery) isdeemed to be suitable for re-use, then the dispenser 2 will allow asubsequent shopper to select that terminal for use in his or hershopping functions. The dispenser 2 also comprises a control processingsection 12, a memory section 14, a printer 16, a card reader 18, a hostI/O section 20, and a display 22, all of which will be described belowin further detail.

When a shopper desires to obtain a scanning terminal 100 from thedispenser 2, he accesses the system by presenting a coded identificationcard to the card reader portion of the dispenser 2. The card reader maybe a magnetic stripe reader, which is well known in the art. In thiscase, the shopper presents a "loyalty card" having an associated encodedmagnetic stripe, comprising data indicative of the identity of theshopper. The shopper may also present a credit card, smart card, debitcard or the like having a suitable encoded magnetic stripe. In analternative embodiment, the card reader 18 may be a bar code symbolscanning device, suitable for reading a one or two dimensional bar codesymbol printed on a loyalty card, driver's license or the like, forobtaining therefrom the required identification data. Any type oftechnology which lends itself towards the use of automaticidentification may be implemented by this system.

Once the control section 12 of the dispenser 2 has determined that therequesting user is allowed to access a terminal 100 (i.e. the shopper isa member of the self-service shopping system), a terminal is assigned tothe user and the identity of the assigned terminal is signaled to theuser in any of various ways. For example, an LED associated with and inclose proximity to the assigned terminal may be caused to blink on andoff, thus catching the attention of the shopper and indicating that heshould select that terminal. Likewise, appropriate instructions may bedisplayed to the shopper via the display 22, such "Please take terminalnumber 17" or the like. Concurrently therewith, a locking mechanismwhich may be used for terminal security purposes to prevent unauthorizedremoval of the terminal will be disabled by the dispenser control logic12, thus enabling the removal of the assigned terminal 100 from thedispenser 2 by the shopper.

The scanning terminal 100 shown in block diagrammatic form in FIG. 4 isa lightweight, portable, hand-holdable device well suited for carryingby the shopper and performing data entry functions such as keypad entryand/or bar code scanning of items selected for purchase. The terminal100 comprises a scanning module 102, a decoder 104, a keypad 106, adisplay 108, a dispenser interface section 110, a control section 112,an items scanned memory section 114, a price look-up table 116, and,optionally, a wireless transceiver 118 and antenna 120, all of whichfunction in operative association with bus 122 as further described.

The scanning module 102 and decoder 104 operate in conjunction in amanner well known in the art to allow the user to scan a bar codelocated on an item selected for purchase and input onto bus 122 forsubsequent processing. For example, in the preferred embodiment, thescanning module 102 is a laser bar code scanner which utilizes a laserlight source, a scanning means such as a mirror mounted on a miniaturemotor, and a photosensor for receiving light reflected from a target barcode and for converting the received reflected light into an electricalsignal indicative of the degrees of reflectivity of the various portionsof the bar code. The scanning module also comprises signal processingwhich digitizes the signal from the photosensor so that the decoder mayperform an analysis thereon to determine the data represented by the barcode. A laser scanning device such as this is well known in the art andmay be found, for example, in U.S. Pat. No. 5,479,000, which isincorporated by reference herein. In addition, the scanning module maybe of the CCD type, which utilizes a linear or two-dimensional CCD arrayfor capturing the reflected light (ambient or otherwise) from the targetbar code and for generating an associated signal which is processed inaccordance with techniques well known in the art.

After the user has scanned a bar code from a target item, the decodeddata signal indicative of the data represented by the target bar code isoutput by the decoder onto the bus 122. The decoded data is used tofetch price and item description information from the price look-uptable 116, which is in turn sent to the display 108 for display to theuser. The price and description data is also sent to the item scannedmemory 114 for storage therein such that the item scanned memory 114will compile a tally list of all items scanned by the user in thatshopping trip.

If desired, the user may delete an item from the tally list by scanningthe bar code of the item and depressing an appropriate function keyfound on the keypad, e.g. a "minus" key, to signal to the control logic112 that the associated scanned bar code is to removed from, rather thanadded to, the tally list in the memory 114. Thus, when the user changeshis mind about the purchase of an item scanned, he may re-scan the item,press the appropriate return key, place the item back on the shelf, andthe tally list will reflect accurately only those items intended to bepurchased by the user.

The user may, at any desired time, obtain a subtotal of the itemsscanned for purchase and resident in memory 114 by depressing anappropriate key on the keypad 106, e.g. a "subtotal" key. This key willcause the control logic section 112 to fetch the price of each item fromthe memory 114, add the prices together, and display the total on thedisplay 108. This enables the user to ensure that he has not exceeded acertain spending limit.

In another embodiment, as an alternative to looking up the price anddescription of the scanned item from a terminal-resident memory such asthe look-up table 116, the terminal 100 may employ wirelesscommunication with the host computer 4 via the optional wirelesstransceiver 118 and antenna 120. In such an embodiment, the price anditem description information is stored in the price look-up table 210 atthe host computer 4, as shown in FIG. 5. The decoded bar code data issent via the transceiver 118 to the associated antenna 203 andtransceiver 202 at the host computer 4, which fetches the price and itemdescription from its price look-up table 210 and sends it back to theterminal 100 via the wireless link. This type of embodiment eliminatesthe need for a look-up table to be stored in each terminal 100, andchanges to the data in the price look-up table may be made at the hostrather than requiring each terminal 100 to be revised when the price oritem description is changed.

In addition, when a wireless data link is used to allow communicationsbetween the terminal 100 and the host 4, then the tally list of itemsscanned may be kept in an appropriate memory location in the itemsscanned memory 212 at the host computer 4 rather than utilizing anon-board memory 114. Deletion of an item and acquisition of a subtotalmay be likewise executed through the wireless link rather thanperforming those functions at the terminal 100.

The wireless link may be accomplished via an RF (radio frequency) link,which is well known in the art and is described in detail in U.S. Pat.No. 5,157,687; which is incorporated by reference herein. In an RF basedscenario, the host transceiver would likely be physically located nearthe host since communications with the terminals need not be in closeproximity. In the alternative, other wireless technologies such asinfrared communications may be implemented, with transceiver stationsstrategically located throughout the store for communications with eachterminal 100 as the shopper proceeds through the store.

In still another embodiment, the terminal 100 may be used in conjunctionwith a kiosk located in the store to provide the customer forinformation purposes only a list of the items scanned, the item pricesand the cost of purchase. In such case, the terminal 100 maybe a "dumbterminal" and simplified to eliminate costly functions, such as control,display and other functions. The kiosk contains a receptacle forreceiving the terminal; a display and a keyboard for communication withthe host computer. In use, the terminal would be loaded into thereceptacle and the scanned items loaded into the host computer forprocessing in accordance with customer inputs through the keyboard. Theprocessing results would be shown on the display. The kiosk maycommunicate with the host computer by wireline or wireless link, aspreviously indicated. As an alternative, the customer may load the "dumbterminal" into a cradle at the checkout stand whereupon the scanneditems would be loaded into the host computer for check-out processing.

Returning to FIG. 3, when the shopper has completed scanning items forpurchase, terminal 100 is returned back to the scanner dispenser 2 andplaced within an appropriate mating recess for communications with thescanner interface 10. When the terminal has implemented an on-boardlook-up table 116 and memory 114, then the tally list of items scannedis downloaded from the memory 114 to the host computer 4 for furtherprocessing. Along with the tally list, data indicative of the identityof the shopper, which is obtained when the shopper initially requests aterminal 100 from the dispenser 2 as described above, is downloaded tothe host 4. The host 4 thusly has stored therein the identity of theshopper along with data indicative of the items selected for purchase.If the system is operating in wireless communications mode, then theitems scanned memory 212 at the host computer will contain the tallylist of items scanned for purchase for that particular shopper withoutthe need for downloading from the scanning terminal at the dispenserinterface.

After determining that the shopper has completed selecting and scanningitems for purchase, the host computer proceeds to determine, inaccordance with the present invention, the items to be checked by thecheckout cashier (or security guard or the like). Referring to FIG. 5,the host computer has stored in a security criteria memory 214 aplurality of security criteria which are used to determine the items tocheck by the cashier, if the shopper is determined to be audited. Thesecurity criteria include, but are not limited to, the following:

1. Shopper frequency: The frequency of visits of the shopper is a factorto consider in determining the number of items to check. A counter iskept in memory for each member of the self-service system, which isincremented every time the shopper has visited the store. In general,the more the shopper has visited the store, the lower the number ofitems will be checked and the probability of checking the shopper willbe lower, since so-called loyal shoppers will be given the benefit ofhaving less items checked.

2. Queue length: The host computer will know the approximate length ofthe queue by observing the number terminals have been used and returnedto the dispenser, but which have not yet been checked out at the POSterminal. Since a goal of the system is to maintain a high throughput ofshoppers, it may be postulated that less items will be checked when thequeue length is long.

3. Prior history: The specific prior history of the particular shopperis stored and used to factor in the determination of the number of itemsto be checked. That is, shoppers with a prior history of scanningerrors, as determined by the security check at the POS terminal, willhave more items checked than shoppers with less errors in the past andthe probability of re-scanning items of such shopper is higher.

4. Store location: Demographic indicia linked to the likelihood thatpilferage will occur more frequently in a certain geographic locationmay be factored into the determination of the number of items to bechecked.

5. Time of day/ day of week/ date of year: Statistical analysis ofpilferage as it may be linked to the time of day, day of week or date ofyear may be factored into the determination.

6. Item returns: The host computer will have information available to itas to the number of times a shopper has depressed the minus key, whichindicates a scanned item has been returned to the shelf. A likelihoodexists that a shopper who has depressed this key an excessive amount oftimes is more likely to have failed to actually return the item to theshelf. Thus, the number of items to be checked should increase as thisfactor increases. This factor increases the probability of re-scanningand the number of items to be checked.

7. Dwell time between scans: The elapsed or dwell time between scans bythe shopper may be examined by time-tagging the scans and analyzing theshopping pattern. Thus, for example, it may be statistically determinedthat shoppers should scan an item once every minute. When a shoppertakes five minutes to scan the next item, it may be presumed that itemsmay have been selected for purchase but not scanned in that interim.Those shoppers with inordinate dwell times may have more items checked.

After the host computer 4 has used the security criteria as describedabove in order to ascertain, via the security determination logic means216, the specific number of items to check for scan accuracy by thecashier or security guard, it proceeds to determine if this shopper isto be re-scanned (audited) or not, and if so, which types of items thecashier or security guard should look for in selecting the items tocheck. Factors to consider in determining which items to look for fromamong the shoppers purchases include the following:

1. Statistical determination of highly pilfered items: Historically,certain items such as batteries or razors (high cost, small packagesize) have a higher percentage of pilferage than other items such aswatermelons (low cost, large package size).

2. Prior history: The specific prior history of the particular shopperis stored and used to factor in the determination of which items shouldbe checked. That is, shoppers with a prior history of scanning errorsfor certain items, as determined by the security check at the POSterminal, will have those particular items checked.

3. Store location: Demographic indicia linked to the likelihood thatpilferage of certain items will occur more frequently in a certaingeographic location may be factored into the determination of thespecific items to be checked.

4. Time of day/ day of week/ date of year: Statistical analysis ofpilferage of certain types of items as it may be linked to the time ofday, day of week or date of year may be factored into the determination.

5. Item returns: The host computer will have information available to itas to the number of times a shopper has depressed the minus key forcertain items, which indicates that scanned item has been returned tothe shelf. A likelihood exists that a shopper who has depressed this keyan excessive amount of times is more likely to have failed to actuallyreturned the item to the shelf, and thus that item should be checked.

6. Dwell time between scans: The elapsed or dwell time between scans bythe shopper may be examined by time-tagging the scans and analyzing theshopping pattern. Thus, for example, it may be statistically determinedthat shoppers should scan an item once every minute. When a shoppertakes five minutes to scan the next item, it may be presumed that itemsmay have been selected for purchase but not scanned in that interim. Byanalyzing the store location as a function of dwell time increase (bychecking adjacent scans and extrapolating the interim location of theshopper), it can be determined which items should be checked.

Once the analysis has been made by the host computer as to whichspecific (or types of) items should looked for by the cashier orsecurity guard, then data indicative thereof is stored along with thenumber of items to be checked for that shopper in the memory of the hostcomputer 4. This data is available for download to the appropriate POSterminal selected for final checkout by the shopper after he has resumedthe scanning terminal 100 to the dispenser 2.

The shopper may then proceed to an appropriate POS terminal 6, which ismanned by a cashier for tender of payment and security checking of theitems selected for purchase. When the shopper reaches the POS station,he presents his loyalty card (or other suitable automatic identificationcard) to the cashier, who will present the card to an appropriate cardreader for automatic identification of the shopper. The shopper'sidentification data is used to fetch from the host computer 4 the tallylist of items scanned and the data indicative of the number of items tobe checked as well as the identity of specific items or types of itemsto look for in performing the audit process.

The cashier or security guard reads from the display at the POS terminalthe list of items to check (or from a printed version of the list) andselects the items for checking. The cashier scans the bar code of eachitem, and if any item scanned is not on the tally list, the cashier orsecurity guard is alerted that the shopper has made an error inscanning. In this case, the retail establishment may opt to re-scan theentire shopping cart, may simply add the item to the tally list, or maytake some other act it deems appropriate for the situation. Dataindicative of the mis-scanned item is then transmitted from the POSterminal back to the host computer and stored in its security criteriamemory 214 for subsequent processing and subsequent criteriadetermination.

Additional Self Checkout Auditing Processes or Policies

A more advanced auditing policy will increase security and furtherreduce labor. Many factors can be used in developing such a policy. Someof the factors that can be used in an advanced auditing policy arelisted hereinafter in Section A. How these factors are taken intoaccount by a system operator determines the auditing policy. Severalauditing policies are proposed. One policy is based on statisticaldecision theory and is described in Section B1. A policy based ondifferent customers response to auditing is given in Section B2. Apolicy based on Neural Networks is described in Section B3.

A. Factors used in an Advanced Auditing Policy

There are many heuristic rules that can be applied in an auditingpolicy. In the prior art there is one implicit rule. The rule can bestated something like this: People who have failed an audit (either dueto theft or due to innocent errors) are more likely to do so in thefuture, therefore they should be audited more often. To develop a betterauditing policy we need to use additional rules that model the realworld more completely.

In this section we summarize the rules that will be used in subsequentsections to develop advanced auditing policies. The following is a listof the rules and a short description of each.

Audit History:

The audit history of a given customer is a good indication of his futurecheckout accuracy. One may use the recent audit history or possibly theentire audit history. This is the rule used in the prior art.

Loyal Customers:

Customers who shop frequently at the store are likely more honest andshould be audited less. Some of this falls out from the previous rule;however, we may want to give the loyal customer an additional level oftrust and further reduce the audit frequency.

Regional Differences:

Different regions of the country or the world will have differentlikelihood of theft. Regions with a high theft level may require morestringent security so the level of auditing should be higher in thoseregions.

Store Shopping Activity:

To reduce waiting a long time in audit lines the store may want toreduce the average auditing level during times when the store is verybusy. This will increase the throughput of the store at the risk ofslightly increased theft.

Weight & Size of Items:

These qualities may be factor in the rate of pilferage.

Seasonal:

During different seasons people may be more likely to attempt theft ormake errors.

Time of Day:

During different times of the day people may be more likely to attempttheft or make errors.

High Rate of Returns:

If the customer is deleting a lot of items from the checkout list he maybe more likely to be attempting theft.

Scan Frequency:

Long time spans between scan items could be an indication of attemptedtheft.

B1. Statistical Decision Theory Based Auditing Policy

The approach taken here is to use statistical decision theory toimplement an auditing policy based in whole or in part on the aboverules. A description of statistical decision theory is given in AppendixI, references [1, 2].

The first parameter that is required in setting up a decision rule is adescription of the state space, which is the set of possible states ofnature [2]. This is all the information that we have at our disposal tomake a decision. The state space, θ, which we will use consists of afinite set of possible events that can occur. As an example we willconsider a state space of four events which consist of all combinationsof two independent events. The first event to consider is whether thereis an error in the self checkout that was performed by the customer.This event, E.OR right.θ, represents an error in the self checkout data;its complement E.OR right.θ, means there is no error. The second eventindicates whether the store is busy or not. The event, B.di-electcons.θ, means the store is busy and the event B.OR right.θ means thestore is not busy.

Taking combinations of E, E, B, and B, the state space can bepartitioned into four mutually exclusive events,

    θ={θ.sub.1, θ.sub.2, θ.sub.3, θ.sub.4 }(1)

where the states are as follows,

    θ.sub.1 =E∩B

    θ.sub.2 =E∩B

    θ.sub.3 =E∩B

    θ.sub.4 =E∩B                                 (2)

So θ₁ means that the customer has made an error in his checkout and thestore is busy, θ₂ means self checkout is correct and the store is busy,etc.

In developing a decision rule we also need to define the possibleactions that we can take. Since we are trying to determine whether toaudit a customer or not, our actions involve levels of auditing. Eachtime a customer shops at the store we have several options. We can letthe customer leave without an audit, we can audit the customers entirecart, or we can perform a limited audit by checking only some of theitems the customer has in his cart.

For this example, we will consider a action space consisting of thesethree actions,

    A={a.sub.1, a.sub.2, a.sub.3 }.                            (3)

The three actions are,

    a.sub.1 : No Audit

    a.sub.2 : Limited Audit (e.g. check 10% of items)

    a.sub.3 : Full Audit                                       (4)

Finally, we need to define a loss function associated with taking actiona_(i) given that event θ_(j) occurs. This loss function measures theloss (either financial or otherwise) incurred by taking such an action.In the self checkout problem there are several factors that effect loss.First, there is the actual loss incurred due to theft. There is a lossassociated with a low throughput of customers that may incur due toexcessive auditing during busy times. There is the labor costsassociated with performing the audit. And finally, there is lossassociated with customer dissatisfaction due to excessive auditing orhaving to wait a long time.

As an example, Table 1 lists the different losses that occur. The actualvalues for that table would depend on what store this system is beingused in and could be adjusted to fit their circumstances. Combining thelosses listed in this table it is possible to build a loss function. Asan example, loss function L(θ, a) is given in Table 2.

Lets see in Table 2 how the loss function is built up from the losses inTable 1. For example, whether the store is busy or not, there is anaverage inventory loss of $5.00 if the customer has made an error in hisself checkout and we chose not to audit. If he has not made an error andwe do not audit then their is no loss since there is no error to find.There are no other types of losses when you do not perform an audit. Ifthe store is busy and we chose to do a limited audit, and the customermade an error, then we have a $2.00 loss of inventory, a $0.50 loss inpotential sales, a loss of $0.50 in labor costs, and finally a loss of$0.50 in customer dissatisfaction. Under the same conditions, exceptthat the customer did not make an error, then we have all the samelosses, except the $2.00 loss of inventory. The other terms of the lossfunction are developed in a similar manner. So in this loss function wehave summarized the four types of loss: inventory loss, potential salesloss, labor costs, and customer dissatisfaction.

Now, given the state space, the action space, and the loss function weneed a criterion for selecting the "best" action. We choose to selectthe action that gives the smallest loss on the average. We cannot beguaranteed to always select the smallest loss because we do not know ifthe customer has made an error (if we did it would make the auditdecision very easy). This lack of knowledge can be modelledstatistically. We model the events in the state space as random and withsome distribution as to their likelihood. Now in the state space we haveset up there are two factors: whether the customer has made an error andwhether the store is busy. The first is an unknown and the second isknow. So in this case all we have to model as random are the events Eand E.

There are many possible models that could be used to represent thisprobabilistically. For example, it could change with time and otherfactors. We will make the simple assumption

                  TABLE 1                                                         ______________________________________                                        Different Losses Occurring In A Loss Function                                 Loss                  Value                                                   ______________________________________                                        Inventory loss when no audit is                                                                     $5.00                                                   performed and customer made an                                                error                                                                         Inventory loss when limited audit                                                                   $2.00                                                   is performed and customer made an                                             error                                                                         Inventory loss when full audit is                                                                   $0.00                                                   performed                                                                     Potential sales lost when store is                                                                  $1.00                                                   busy and full audit is performed                                              Potential sales lost when store is                                                                  $0.50                                                   busy and partial audit is                                                     performed                                                                     Potential sales lost when store is                                                                  $0.25                                                   not busy and partial audit is                                                 performed                                                                     Potential sales lost when no audit                                                                  $0.00                                                   is performed                                                                  Labor costs for full audit                                                                          $1.00                                                   Labor costs for partial audit                                                                       $0.50                                                   Labor costs for no audit                                                                            $0.00                                                   Customer dissatisfaction with full                                                                  $1.00                                                   audit when store is busy                                                      Customer dissatisfaction with                                                                       $0.50                                                   partial audit when store is busy                                              Customer dissatisfaction with full                                                                  $0.50                                                   audit when store is not busy                                                  Customer dissatisfaction with                                                                       $0.25                                                   partial audit when store is not                                               busy                                                                          Customer dissatisfaction with no                                                                    $0.00                                                   audit                                                                         ______________________________________                                    

                  TABLE 2                                                         ______________________________________                                        Types of losses and their financial value                                             θ.sub.1 = E ∩                                                          θ.sub.2 = E ∩                                                            θ.sub.3 = E ∩                                   B      B        B         θ.sub.4 = E ∩ B               ______________________________________                                        a.sub.1                                                                           (No audit)                                                                              5.0      0.0    5.0     0.0                                     a.sub.2                                                                           (Limited  3.5      1.5    3.0     1.0                                         Audit)                                                                    a.sub.3                                                                           (Full     3.0      3.0    2.0     2.0                                         Audit)                                                                    ______________________________________                                    

that for each customer there is a probability that he will make anderror and that probability is fixed. Later we could use a more elaboratemodel. So for a given customer we will define the p_(e) as thatprobability of making such an error,

    p.sub.e =P(E)=1-P(E)                                       (5)

If we know this probability (and actually we have to estimate it) we canuse it to find the best action. To do this we have to define the Bayesloss [2] as the average loss, ##EQU1## where L represents loss which isa function of types of losses and the auditing decision of Table 2.

Since we can assume that we know whether the store is busy or not we cansimplify this formula. If the store is busy we have, P(B) =1 and P(B)=B.So,

    B(a)=L(θ.sub.1,a)P(θ.sub.1)+L(θ.sub.2,a)P(θ.sub.2)+L(θ.sub.3, a)P(θ.sub.3)+L(θ.sub.4, a)P(θ.sub.4)

    B(a)=L(θ.sub.1,a)P(E)+L(θ.sub.2,a)P(E)

    B(a)=L(θ.sub.1, a)p.sub.e +L(θ.sub.2, a)(1-p.sub.e)(7)

And if the store is not busy we have, P(B)=0 and P(B)=L. So,

    B(a)=L(θ.sub.1,a)P(θ.sub.1)+L(θ.sub.2,a)P(θ.sub.2)+L(θ.sub.3, a)P(θ.sub.3)+L(θ.sub.4, a)P(θ.sub.4)

    B(a)=L(θ.sub.3,a)P(E)+L(θ.sub.4,a)P(E)

    B(a)=L(θ.sub.3, a)p.sub.e +L(θ.sub.4, a)(1-p.sub.e)(8)

The best action to take is the action that minimizes the Bayes loss [2].That action is called the Bayes action, a_(B),

    B(a.sub.B)=min.sub.j B(a.sub.j)                            (9)

We will now show how to get the probability of this customer making anerror. We will use our prior audit history to estimate that probability.The simplest estimator is just the relative frequency. If the customerhas been audited N_(a) times and has failed the audit (i.e. had anerror) N_(e) times then we can estimate the probability of an error asthe ratio of those two numbers, ##EQU2## We may choose to only use therecent audit history so that if p_(e) has changed recently we can detectit. If we want to do that then we use only the last 10 audits forexample, and then N_(a) =10 and N_(e) is the number of errors in thoseaudits.

In summary, one estimates p_(e) using Equation 10. If the store is busyone uses Equation 7 for the Bayes loss, substituting p_(e) for p_(e),##EQU3## If the store is not busy one uses Equation 8 for the Bayesloss, substituting p_(e) for p_(e), ##EQU4## Select the Bayes action,a_(B), that minimizes the Bayes loss as in Equation 9, ##EQU5##

In the example given here only two variables were considered: thecustomer making an error and whether the store is busy. Clearly theother factors listed in Section A.2 can all be included in the auditingpolicy. This involves having more variables in the loss function. Note,however that the only unknown is whether the customer has made an error.So the only probability that has to be estimated is the probability ofsuch an error. All other parameters of the loss function would be knownto the system. For example, the system would know whether it was beingused in a safe neighborhood or not. So the calculation of the Bayesaction would still be quite simple.

In this example we have made a number of simplifications. For example,we have described the business of the store as either busy or not busy.We could easily have many levels of how busy the store is and define aloss function that takes into account all those different levels.

Using statistical decision theory we always select the action that givesthe lowest average loss. From a mathematical point of view this is theoptimum thing to do. However, if a customer has a relatively fixedprobability of error and visits the store under similar conditions everytime (e.g. level of business) then it is likely that we will take thesame action most of the time. This may not be the best thing to do froma psychological point of view. The customer may start to try to secondguess the system and change his actions. For this reason we may want torandomize our actions a bit. One way to do this is to assign aprobability of performing each action and then select the actionaccording to that probability law. Let us define p_(i) as theprobability of taking action a_(i). Then one way to select thoseprobabilities so that they are inversely proportional to the lossassociated with taking that action, ##EQU6## Then we take action a_(i)with probability p_(i). This may not be the optimum from a mathematicalpoint of view but it may be better from a psychological point of view.

B2. Audit Policy Based on Customer's Response to Audit

Another approach that can be taken in developing an auditing policy isto quantify the customers reaction to being audited and use his reactionto govern your audit policy.

To illustrate this idea we will define A as the cost of performing anaudit. Let L(c) be the expected inventory loss associated with customerc if an audit is not performed. This expected loss takes into accountboth the average loss if an error is made and the probability that hewill make an error. The objective of this auditing policy it to select aprobability of performing an audit to minimize the average overall loss.Then once that probability is calculated an audit is performed with thatprobability. Let the probability of performing an audit be Pa. If theloss is independent of the probability of performing an audit then theaverage loss is given by,

    L.sub.ave =p.sub.a A+(1-p.sub.a)L(c).                      (15)

This is easy to see since you perform an audit with probability p_(a)and when you do it costs A; similarly you skip an audit with probability(1-p_(a)) and it costs L(c) each time. So we have just averaged thosetwo possibilities.

What if L(c) is also actually a function of the audit probability? Thenwe write the inventory loss as a function of both the customer and thefrequency of auditing, so the loss has two parameters: L (c,p_(a)).There are many possible functions that can be used to model thecustomers response to being audited. For example, many customers will bemore likely to make less errors if they are audited, whereas others maycontinue to make errors even if audited frequently. Let us try thefollowing loss function,

    L(c,p.sub.a)=B(c)(1-p.sub.a).sup.n                         (16)

where B(c) is the loss when the customer is never audited and n ispositive integer. The larger n the more likely the customer will makeless errors when audited frequently. So a loss function with a largevalue of n models a customer who is highly deterred from making an errorby the threat of an audit. This can be seen by noticing that for large nthe loss, L(c), drops off faster with an increase in p_(a). In this waywe can model different customers response to being audited.

Given this formula for L(c,p_(a)) we can select p_(a) to minimize theaverage loss. Substituting this new inventory loss function intoEquation 15 for average loss we get,

    L.sub.ave =p.sub.a A+(1-p.sub.a)L(c, p.sub.a)

    L.sub.ave =p.sub.a A+(1-p.sub.a).sup.(n+1) B(c).           (17)

To minimize this we differentiate with respect to p_(a) and set it equalto zero,

    A-(n+1)(1-p.sub.a).sup.n B(c)=0                            (18)

which can be solved for the probability, ##EQU7## Since a probabilitymust be nonnegative we must have,

    A<(n+1)B(c)                                                (20)

and if we do not satisfy that equation we should just set p_(a) =0. ThusEquation 19 gives the formula for the optimum audit probability.

In Equation 15 the only probability density function was p_(a). If thelosses A and L were also random we can generalize Equation 15 byaveraging over those distributions also,

    L.sub.ave =∫∫(p.sub.a A+(1-p.sub.a)L(c))p(a)p(l)dadl(21)

where p(a) is the probability density function of A and p(l) is thedensity function of L. Then the same procedure as above is applied tofind the best auditing probability.

B3. Artificial Neural Network Based Auditing Policy

Artificial Neural Networks have been applied to sales forecasting,industrial process control, customer research, data validation, riskmanagement, target marketing, medical diagnosis and other problems werethe system transfer function depends in the input variables, and changesover time. For example, Mellon Bank installed a neural network creditcard fraud detection system and the realized savings were expected topay for the new system in six months. See Appendix I, references [3, 4].Essentially, a Neural Network is used to learn patterns andrelationships in data. The data here can be information about thecustomer and the output can be whether or not the customer is to beaudited.

The input variables may be fuzzified. For example, the customer's agemay be fuzzified to {teenager, young, middle aged or aged.} The shape ofthe membership functions may be learned and generated via a neural netin order to minimize some cost function--usually from a teacher(expert). The fuzzy rule base may also be adaptable over time. GeneticAlgorithms may be applied to this rule base such that rules that workwell will mutate and spawn children that will perform better over time.See Appendix I, reference [5]. References on Genetic Algorithms may befound in texts that describe "Evolutionary Programming" concepts.

Summarizing, the advanced auditing policies of the present invention, asreflected in the auditing rules which model the real world andstatistical decision theory based auditing, is not to audit everycustomer. Instead, the present invention determines whether a givenshopper or customer is to be audited on a given shopping trip based uponobtaining a minimum checkout loss for such customer thereby providing anaudit policy which is not too intrusive to customers or shoppers, butwhich is a significant deterrent to theft and unintentional errors.

Appendix 1

References

[1] B. W. Lindgren, Elements of Decision Theory. Macmillen Co., 1971.

[2] B. W. Lindgren, Statistical Theory. Macmillen Co., third ed., 1976.

[3] Z. Solutions, "An introduction to neural networks."http://www.mindspring.com/zsol/mgrguid.html.

[4] N. M. McCord and W. T. Illingworth, A Practical Guide to NeuralNets. Addison-Wesley, 1990.

[5] "Genetic algorithms archive." http://www.aic.nrl.navy.mil/galist/.

It should be apparent to those skilled in the art that while theinvention has been described with respect to a specific embodiment,various changes may be made therein without departing from the spiritand scope of the invention as described in the specification and definedin the claims.

We claim:
 1. A method for enhancing the accuracy and reducing auditlevel of a self-checkout system while achieving a lowest average lossfor a customer in a system wherein a customer selects a plurality ofitems for purchase and registers the plurality of items with a portableterminal, comprising the steps of:a) generating at least one state spaceof possible events that may occur in a self-checkout by a customer; b)generating at least one action space in response to each state space; c)generating a loss function in response to each action space; and d)auditing a self-check out to obtain a minimum checkout loss for acustomer.
 2. The method of claim 1 wherein the state space is astatistical representation of possible events (E) that may occur in aself-checkout.
 3. The method of claim 1 wherein the action space is astatistical representation of possible actions that may be taken inresponse to the possible events.
 4. The method of claim 1 wherein theloss function measures a loss resulting from an action.
 5. The method ofclaim 1 further comprising the step of determining and defining anaverage loss for a self check-out as a Bayes loss.
 6. The method ofclaim 5 where the Bayes average loss is given by the equation: ##EQU8##where θ_(i) represents random events.
 7. The method of claim 1 whereinthe loss function is a function of L(c) and p where L(c) is the expectedinventory loss associated with a shopper (c) and p is the probability ofperforming an audit on the shopper.
 8. In a self-service shoppingcheckout system wherein a shopper is provided with a portableself-scanning terminal for scanning of a bar code of an item selectedfor purchase prior to depositing the item into a shopping cart, andwherein a list of items self-scanned by the shopper is compiled and madeavailable to a cashier for surveillance and payment purposes, theimprovement comprising a method for enhancing the accuracy and reducingoverall audit level and achieving a lowest average loss of aself-checkout system wherein a customer selects a plurality of items forpurchase and registers the plurality of items with the portableterminal, comprising the steps of:a) determining if the customer is tobe audited to obtain the lowest average loss for the customer usingstatistical decision analysis; a) determining, as a function of aplurality of input criteria, the number of items n to be scanned; b)selecting from the shopper's cart of items presented for purchase nitems to be scanned; c) scanning a bar code located on each of said nitems selected for scanning; and d) allowing the shopping transaction ifeach item selected for scanning is present on the list of self-scanneditems compiled by the shopper.
 9. The method of claim 8 furthercomprising the step of disallowing the shopping transaction if theminimum checkout loss exceeds a threshold.
 10. A self-service shoppingcheckout system comprising:a) a plurality of portable self-checkoutdevices, each of said self-checkout devices to be used by a customer toscan a bar code located on an item to be purchased so as to recordtherein a list of such items to be purchased, each of said devicescomprising bar code scanning means for scanning and decoding a bar codelocated on an item to be purchased, means for compiling a list of itemsscanned by said customer, and a data output port for allowing transferof said scanned item list to an associated data port located external tosaid portable device; b) a stationary dispenser unit for the releasablecontainment of said plurality of portable self-checkout devices, saiddispenser unit comprising:(i) a plurality of device containment slots,each of said slots being configured for releasable containment of amating self-scanning device, each of said slots having associatedtherewith a data input port suitable for mating with a data output portlocated on a portable self-checkout device; and (ii) a printer forprinting a tally list of items scanned for purchase by said shopper,said tally list being supplied by a self-checkout device after saidself-checkout device is returned to a device containment slot afterbeing used by a shopper, said tally list further comprising atwo-dimensional bar code encoded with said items scanned by saidshopper, a unique identification record associated with said shopper,and the number of items scanned by said shopper; c) a plurality of pointof sale terminals, each of said point of sale terminal comprising:(i)bar code reading means for reading said two-dimensional bar code from atally list presented to a cashier operating said point of sale terminal,said bar code reading means providing as output data signalsrepresenting said items scanned by said shopper, said uniqueidentification record associated with said shopper, and said number ofitems scanned by said shopper; said bar code reading means alsoconfigured so as to scan select items presented for checking by saidcashier; (d) means for generating at least one state space of possibleevents (E) that may occur in a self-checkout by a shopper; (e) means forgenerating an action space in response to each state space; (f) meansfor generating a loss function in response to each action space; and (g)means for auditing a self-check out to obtain a minimum checkout lossfor a customer.
 11. A method of selecting a best action for auditing acustomer in a self-check out system comprising the steps of:a)generating at least one state space of possible events that may occur ina self-checkout by the customer; b) generating at least one action spacein response to each state space; c) generating a loss function inresponse to each action space; and d) selecting the action that givesthe smallest loss function on the average.
 12. A method of selecting abest action for auditing a customer in a self-check out systemcomprising the steps of:a) generating at least one state space ofpossible events that may occur in a self-checkout by the customer; b)generating at least one action space in response to each state space; c)generating a Bayes' loss function in response to each action space; andd) randomly selecting the action with the probability inverselyproportional to the Bayes' loss associated with taking that action. 13.A method of selecting a best action for auditing a customer in aself-check out system comprising the steps of:a) determining if thecustomer is to be audited for items on a given shopping trip based upona lowest average loss to the self-check out system; and b) determininghow many items to audit based on a statistical decision analysis appliedto self shopping if step a) determines the customer should be audited.14. In a self-check out system, a method of determining the average lossfor a customer in a self-check out, comprising the steps of:a)identifying a plurality of factors including store busy; level of audit(full, patial, none) and affecting the self-check out; b) selecting a atleast one of the plurality of the events; and c) calculating the averageloss for the customer in the self-check out using a Bayes' loss factorbased upon the selected at least plurality of events.
 15. A self-serviceshopping checkout system for enhancing the accuracy and reducing overallaudit level while achieving a lowest average loss of a self-checkout bya customer, comprising:a) means for registering a plurality of items bythe customer using a portable terminal; b) means for determining if thecustomer is to be audited to obtain the lowest average loss for thecustomer using statistical decision analysis; c) means for determining,as a function of a plurality of input criteria, which items n and thenumber thereof to be scanned; d) means for selecting from the shopper'scart of items presented for purchase n items to be scanned; e) means forproperly scanning a bar code located on each of said n items selectedfor scanning; and f) means for allowing the shopping transaction if eachitem selected for scanning is present on a list of self-scanned itemscompiled by the shopper.
 16. The system of claim 15 furthercomprising:a) means for disallowing the transaction if any item selectedfor scanning is not present on the list of self-scanned items compiledby the shopper.
 17. An article of manufacture, comprising:a computerusable medium having computer readable program code means embodiedtherein for enhancing the accuracy and reducing overall audit levelwhile achieving a lowest average loss of a self-checkout by a customerin a self-check out system including a plurality of portableself-checkout devices, each of said self-checkout devices to be used bya customer to scan a bar code located on an item to be purchased so asto record therein a list of such items to be purchased, each of saiddevices comprising bar code scanning means for scanning and decoding abar code located on an item to be purchased and means for compiling alist of items scanned by said customer, the computer readable programcode means in said article of manufacturing, comprising: (a) computerreadable program code means for registering a plurality of items by thecustomer using a portable terminal; b) computer readable program codemeans for determining if the customer is to be audited to obtain thelowest average loss for the customer using statistical decisionanalysis; c) computer readable program code means for determining, as afunction of a plurality of input criteria, which items n and the numberthereof to be scanned; d) computer readable program code means forselecting from the shopper's cart of items presented for purchase nitems to be scanned; e) computer readable program code means forproperly scanning a bar code located on each of said n items selectedfor scanning; and f) computer readable program code means for allowingthe shopping transaction if each item selected for scanning is presenton a list of self-scanned items compiled by the shopper or disallowingthe transaction if each item selected for scanning is not present on thelist of self-scanned items compiled by the shopper.